diff --git a/scripts/stage1b_api_debug.py b/scripts/stage1b_api_debug.py
new file mode 100644
index 0000000..868ad89
--- /dev/null
+++ b/scripts/stage1b_api_debug.py
@@ -0,0 +1,191 @@
+"""
+Stage 1b: Chat Template Debugger via OpenAI API
+
+Runs the same write_file prompt through vLLM's /v1/chat/completions endpoint
+(so the chat template IS applied) and captures the raw SSE stream.
+
+Compare with stage1_debug.py (raw generate, no template) to see if the
+chat template itself triggers tool-call tokens or if the model still
+code-dumps.
+"""
+
+import os
+import json
+import httpx
+import time
+
+API_BASE = os.environ.get("API_BASE", "http://localhost:8000/v1")
+MODEL = os.environ.get("MODEL", "/workspace/models/SmolLM3-3B")
+MAX_TOKENS = int(os.environ.get("MAX_TOKENS", "512"))
+TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.0"))
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "write_file",
+            "description": "Write content to a file.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "filename": {
+                        "type": "string",
+                        "description": "Name of the file to write"
+                    },
+                    "content": {
+                        "type": "string",
+                        "description": "The content to write to the file"
+                    }
+                },
+                "required": ["filename", "content"]
+            }
+        }
+    }
+]
+
+messages = [
+    {
+        "role": "user",
+        "content": 'Write "hello world" to /tmp/test.txt'
+    }
+]
+
+# ── First, let's see what the chat template produces ─────────────────────────
+print("[stage1b] Loading tokenizer to inspect chat template rendering...")
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
+
+rendered = tokenizer.apply_chat_template(
+    messages,
+    tools=tools,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+print(f"[stage1b] Chat template rendered prompt ({len(rendered)} chars):")
+print(f"{'═' * 60}")
+print(rendered)
+print(f"{'═' * 60}\n")
+
+# ── Streaming request ────────────────────────────────────────────────────────
+
+print(f"[stage1b] Sending streaming request to {API_BASE}/chat/completions ...")
+print(f"[stage1b] Model: {MODEL}")
+print(f"[stage1b] Tools: write_file")
+print(f"[stage1b] Temperature: {TEMPERATURE}, Max tokens: {MAX_TOKENS}\n")
+
+chunk_count = 0
+tool_call_chunks = []
+tool_name = None
+content_chunks = 0
+reasoning_chunks = 0
+accumulated_args = ""
+all_deltas = []
+
+start_time = time.time()
+
+with httpx.Client(timeout=120.0) as client:
+    with client.stream(
+        "POST",
+        f"{API_BASE}/chat/completions",
+        headers={
+            "Content-Type": "application/json"
+        },
+        json={
+            "model": MODEL,
+            "messages": messages,
+            "tools": tools,
+            "tool_choice": "auto",
+            "stream": True,
+            "max_tokens": MAX_TOKENS,
+            "temperature": TEMPERATURE,
+        }
+    ) as response:
+        print(f"[stage1b] Response status: {response.status_code}\n")
+
+        for line in response.iter_lines():
+            if not line or line == "data: [DONE]":
+                continue
+            if not line.startswith("data: "):
+                continue
+
+            try:
+                chunk = json.loads(line[6:])
+                chunk_count += 1
+
+                if not chunk.get("choices"):
+                    continue
+
+                delta = chunk["choices"][0].get("delta", {})
+                all_deltas.append(delta)
+
+                if delta.get("reasoning"):
+                    reasoning_chunks += 1
+
+                if delta.get("tool_calls"):
+                    for tc in delta["tool_calls"]:
+                        fn = tc.get("function", {})
+                        if fn.get("name"):
+                            tool_name = fn["name"]
+                            print(f"  [chunk {chunk_count}] TOOL CALL NAME: {tool_name}")
+                        if fn.get("arguments"):
+                            tool_call_chunks.append(fn["arguments"])
+                            accumulated_args += fn["arguments"]
+
+                if delta.get("content") and delta["content"]:
+                    content_chunks += 1
+                    # Print first few content chunks for visibility
+                    if content_chunks <= 5:
+                        print(f"  [chunk {chunk_count}] CONTENT: {json.dumps(delta['content'][:100])}")
+
+            except json.JSONDecodeError as e:
+                print(f"  [chunk {chunk_count}] JSON error: {e}")
+
+end_time = time.time()
+
+# ── Summary ──────────────────────────────────────────────────────────────────
+
+print(f"\n{'═' * 60}")
+print("SUMMARY")
+print(f"{'═' * 60}")
+print(f"Total SSE chunks: {chunk_count}")
+print(f"  Reasoning chunks: {reasoning_chunks}")
+print(f"  Tool call arg chunks: {len(tool_call_chunks)}")
+print(f"  Content chunks: {content_chunks}")
+print(f"Total time: {end_time - start_time:.3f}s")
+
+if tool_name:
+    print(f"\nTool called: {tool_name}")
+    print(f"Total tool call args: {len(accumulated_args)} chars")
+    try:
+        args = json.loads(accumulated_args)
+        print(f"✓ Parsed successfully:")
+        for k, v in args.items():
+            print(f"  - {k}: {v if len(str(v)) < 100 else f'{str(v)[:100]}...'}")
+    except json.JSONDecodeError as e:
+        print(f"✗ Failed to parse: {e}")
+        print(f"Raw (first 500): {accumulated_args[:500]}")
+else:
+    print("\n⚠ NO TOOL CALL emitted by the model")
+
+if content_chunks > 0:
+    full_content = ""
+    for d in all_deltas:
+        if d.get("content"):
+            full_content += d["content"]
+    print(f"\nFull content output ({len(full_content)} chars):")
+    print(f"{'─' * 60}")
+    print(full_content[:1000])
+    if len(full_content) > 1000:
+        print(f"... ({len(full_content) - 1000} more chars)")
+    print(f"{'─' * 60}")
+
+# ── Verdict ──────────────────────────────────────────────────────────────────
+print(f"\n{'═' * 60}")
+if tool_name and len(tool_call_chunks) > 0:
+    print(f"✓ MODEL EMITTED TOOL CALL via API (chat template applied)")
+    print(f"  → The chat template triggers the tool-call path")
+    print(f"  → Compare with stage1 raw output to see the difference")
+else:
+    print(f"✗ MODEL DID NOT EMIT TOOL CALL even with chat template")
+    print(f"  → This is a model capability issue")
+print(f"{'═' * 60}")